Machine-learning tools are being used for many complex applications to analyze large amounts of data, such as for image and speech recognition, and one of those tools is the deep neural network (DNN), which has demonstrated promising performance in many domains. DNNs are trained with sample data before they can act as classifiers. However, oftentimes, the DNNs have to be retrained to fine-tune performance or to assimilate larger amounts of training data.
In most situations, training a DNN involves solving a non-convex optimization problem with no analytical solution. Typically, solutions are based on solving this problem via iterative procedures, e.g., stochastic gradient descent (SGD). Despite recent progress in computing infrastructure and implementation optimization, it may still take hours, or even up to days or weeks to train a deep neural network, making hard to retrain and inflexible.
Recognizing people in images is a task that is easy for humans but much harder for computers. Being capable of recognizing a substantial number of individuals with high precision and high recall is of great value in many practical applications, such as surveillance, security, photo tagging, and celebrity recognition.